Final Report
Michael Puerto
11/27/2020
1 Getting Started
Recently, twitch literature has begun characterizing twitch communities through twitch chat, viewership trends, and content but no known projects have used resources that exist outside of twitch to understand how twitch communities manifest and interact with one another.
One subreddit called LivestreamFail (LSF) is a dedicated subreddit where users share these twitch clips, general twitch news, and twitch drama.LivestreamFail is one way smaller streamers become noticed and is a platform that I can use to compare big and small communities. I’m interested in the ways emotes are used between smaller and larger communities because I believe emote meanings and sentiments are being actively redefined. This analysis will be split into an LSF part and Twitch emote-sentiment part.
This analysis will investigate users, posts, and comments (sentiment and topic) on r/LivestreamFail and then investigate the comment data and emote use from twitch clips that were featured in LSF posts.
unfold the code block to see which libraries are used
library(pacman)
p_load(tidyverse,
tidytext,
tm,
lubridate,
stringr,
text2vec,
jsonlite,
widyr,
quanteda,
visNetwork,
igraph,
ggraph,
DT,
ggthemes,
readtext)2 Reddit Data: r/LivestreamFail
LSF STUFF SHOULD GO HERE.
3 Twitch
3.1 Twitch: Data collection
tl;dr - twich dmca issues, streamer bans, and the app I used prevented me from downloading alot more data. Next time, I’ll work with the twitch api directly.
Reddit data was gathered using python and PRAW (Python Reddit API Wrapper) to gather recent data from r/LivestreamFail (October 2020). This resulted in over 900 reddit posts. This data was then used in R scrape links and document if the clips had chat available for download.
To actually download the twitch chat, I used application by lay295 and zigagrcar on github found here.
The Digital Millennium Copyright Act is affecting twitch in a big way.
Twitch is currenly in hot water with DMCA claims, and they are banning streamers for repeated streaming “copyrighted” songs. One method streamers use to combat this is by deleting their content shortly after it was broadcasted. This affected data collection since the collected twitch clips were being actively taken down.
This led to the collection of twitch chat from 227 links present in from the reddit posts.
3.2 Emote Data
R and Rselenium was used to scrape the emote data from FrankerFaceZ and BettertwitchTV. Roughly the Top 300 emotes used from each site was collected (emote name and link to image).
This is what a “busy” chat may look like. 
3.2.1 Emote data table
This is a data table with the names and images of the top 600 emotes from both sites (BTTV & FFZ) combined.
bttv emotes need to be updated, also not sure if gif emotes work.
# https://i.stack.imgur.com/kLMaS.jpg
test<-emote_data %>% mutate("emote_image" = paste("<img src=", emote_link, sep = "")) %>%
mutate(emote_image = paste0(emote_image,' height="52"></img>',sep = "")) %>% select(emote_name,emote_image)
datatable(test, escape = FALSE)3.3 Descriptions of twitch chat
This chart shows us how many unique chat lines there are per streamer. This metric is useful for understanding which streamers may be getting the most attention during a point in time on LSF (October in this case). This metric should later be controlled for clip length, since longer clips offer more opprotunity for chat engagement.
data %>% group_by(streamer) %>% count(sort = T)%>%
head(n=10) %>%
ggplot(aes(x = reorder(streamer,-n), y = n))+
geom_col()+
theme_wsj(base_size = 12, color = "green")+
theme(axis.text.x = element_text(size = 12, angle = 15,vjust = .55))+
labs(title = "Which streamer has the most chats?")
This visualization show us the most active twitch chatters in our dataset. In a larger dataset, finding those high-interactcion chatters maybe useful for drawing links between communities or even creating a contributer badges on twith (like the founders badge).
# This creates a !%in% kind of deal
`%notin%` <- Negate(`%in%`)
data %>% group_by(user) %>% filter(user %notin% c("StreamElements","Streamlabs","Nightbot")) %>% count(sort = T) %>%
head(n=10)%>%
ggplot(aes(x = reorder(user,-n), y = n))+
geom_col()+
theme_wsj(base_size = 12, color = "green")+
theme(axis.text.x = element_text(size = 8, angle = 15,vjust = .55),
plot.title = element_text(size = 20))+
labs(title = "Which user has the most chats?")
# Streamelements and streamlabs are bots. This plot will give us further insight in the the demographics of the communities of top 5 streamers. This shows the number accounts created by year for each member of the chat by streamer. As an example, one conclusion that may be drawn is that streamers forsen and Mizkif are not attracting new accounts (New user/ban evaders) to their channels. Another conclusion that may be drawn is that Trainwreckstv in 2018, attracted alot of new users, and perhaps played a significant role in bringing new users to twitch. I should investigate futher to understand what happened with train in 2018. This was perhaps his drama year with MitchJones (A popular WOW streamer) or The Speech.
data %>%
add_count(user,streamer) %>%
filter(n>1,user %notin% c("StreamElements","Streamlabs","Nightbot","Fossabot")) %>%
group_by(user) %>%
add_count(n_distinct(streamer)) %>%
ungroup() %>%
top_n(`n_distinct(streamer)`, n = 2) %>%
arrange(desc(n)) %>%
filter(n >=20) %>%
add_count(user,streamer,name = "#_chat_per_clip") %>%
ggplot(aes(x = user, y = `#_chat_per_clip`, fill = streamer))+
geom_bar(position = "stack",stat = "identity")
data %>% filter(user == "hiteki") %>%
group_by(streamer) %>% count()## # A tibble: 2 x 2
## # Groups: streamer [2]
## streamer n
## <chr> <int>
## 1 Jerma985 3
## 2 MOONMOON 13data %>% filter(user == "hiteki") %>%
summarise(count = n_distinct(streamer))## # A tibble: 1 x 1
## count
## <int>
## 1 2top_5_streamers <- data %>% group_by(streamer) %>% count(sort = T) %>% head(n=5) %>% distinct(streamer)
data %>% filter(streamer %in% top_5_streamers$streamer) %>% mutate(date_year = year(as.Date.character(date))) %>% group_by(date_year,streamer) %>% count(sort = T)%>%
ggplot(aes(x = date_year, y = n, color = streamer)) +
geom_line(size = 2)+
theme_wsj(base_size = 12, color = "green")+
labs(title = "Streamer Communities: Account Creation Dates", subtitle = "Top 5 Streamers")+
theme(plot.title = element_text(size = 15),plot.subtitle = element_text(size= 8),legend.title = element_blank(),legend.position = "bottom")
Tokens, bigrams and trigrams can give us insign into popular emotes/words combinations and spams that occur in these chats.
tokens <- data %>%
unnest_tokens(word,body)%>%
filter(str_detect(word,"^[:alpha:]"))
tokens %>% glimpse(width = 50)## Rows: 159,517
## Columns: 4
## $ user <chr> "Humorous_Chimp", "mayodongs...
## $ date <dttm> 2013-03-29 16:35:36, 2019-1...
## $ streamer <chr> "Jerma985", "Jerma985", "Jer...
## $ word <chr> "omegalul", "out", "of", "mo...tokens %>% group_by(word) %>% count(sort = T)%>%
head(n=10) %>%
ggplot(aes(x= reorder(word,-n),y=n))+
geom_col()+
theme_wsj(base_size = 12, color = "green")+
theme(axis.text.x = element_text(angle = 25))+
labs(title ="Token Counts")
data %>%
unnest_tokens(bigram,body,token = 'ngrams',n = 2)%>%
filter(str_detect(bigram,"^[:alpha:]")) %>%
group_by(bigram) %>% count(sort = T)%>%
head(n=10) %>%
ggplot(aes(x= reorder(bigram,-n),y=n))+
geom_col()+
theme_wsj(base_size = 12, color = "green")+
theme(axis.text.x = element_text(angle = 25,size = 9))+
labs(title ="Bigram Counts")
data %>%
unnest_tokens(trigram,body,token = 'ngrams',n = 3)%>%
filter(str_detect(trigram,"^[:alpha:]")) %>%
group_by(trigram) %>% count(sort = T)%>%
head(n=10) %>%
ggplot(aes(x= reorder(trigram,-n),y=n))+
geom_col()+
theme_wsj(base_size = 12, color = "green")+
theme(axis.text.x = element_text(angle = 25,size = 9))+
labs(title ="trigram Counts")
As you can see, there are alot of duplicated words/phrases. I like to think of these as spams.
Some popular ones are “I was here Pogu I was here Pogu…” or “OMEGALUL OMEGALUL OMEGALUL OMEGALUL..”. One way to combat this is by stripping the chat text to only it’s unique words.
3.4 Comparisons of twitch chat
3.4.1 Word/Emote correlations
corpus_data <- readtext("C:/Users/macia/Documents/MSIA-19/Git/Reddit-and-Twitch/Data Collection/corpus_data.csv", text_field = 'text')
glimpse(corpus_data)## Rows: 35,303
## Columns: 3
## $ doc_id <chr> "corpus_data.csv.1", "corpus_data.csv.2", "corpus_data.csv...
## $ text <chr> "+2", "OMEGALUL", "OUT OF MOJO", "DOOR OMEGALUL", "OMEGALU...
## $ streamer <chr> "Jerma985", "Jerma985", "Jerma985", "Jerma985", "Jerma985"...corpus <- corpus(corpus_data)
dfm <- dfm(corpus, remove_punct=T)
# Select emotes
emotes = emote_data$emote_name
tags = dfm_select(dfm,pattern = emotes)
#tags
toptag = names(topfeatures(tags,30))
# These are the top emotes mentioned in the dataset from the list of popular bttv/ffz emotes
head(toptag)## [1] "omegalul" "pogu" "lulw" "kekw" "pepelaugh" "clap"3.4.2 Emote Combinations
tag_fcm <- fcm(tags)
toptags_fcm <- fcm_select(tag_fcm, pattern = toptag)
textplot_network(toptags_fcm,min_freeq = 0.1, edge_alpha = 0.7, edge_size = 5)
3.4.3 Emote/word combinations
tags = dfm_select(dfm, pattern = c("£","â","<","ó","ðÿ"),selection = "remove")
#tags
toptag = names(topfeatures(tags,20))
tag_fcm <- fcm(tags)
toptags_fcm <- fcm_select(tag_fcm, pattern = toptag)
textplot_network(toptags_fcm,min_freeq = 0.1, edge_alpha = 0.7, edge_size = 5)
3.4.4 Directed Bi-gram network
count_bigrams <- function(data) {
data %>%
unnest_tokens(bigram,"body", token = "ngrams", n = 2) %>%
separate(bigram, c("word1", "word2"), sep = " ") %>%
count(word1, word2, sort = TRUE)
}
visualize_bigrams <- function(bigrams) {
set.seed(2020)
a <- grid::arrow(type = "closed", length = unit(.15, "inches"))
bigrams %>%
graph_from_data_frame() %>%
ggraph(layout = "fr") +
geom_edge_link(aes(edge_alpha = n), show.legend = FALSE, arrow = a) +
geom_node_point(color = "lightblue", size = 5) +
geom_node_text(aes(label = name), vjust = 1, hjust = 1) +
theme_void()
}
viz.bigrams <- data %>%
count_bigrams()
# filter out rare combinations, as well as digits and produce graph
viz.bigrams %>%
filter(n >70) %>%
visualize_bigrams()## Warning in graph_from_data_frame(.): In `d' `NA' elements were replaced with
## string "NA"
word_cors <- tokens %>%
group_by(word) %>%
filter(n() >= 10 ) %>%
pairwise_cor(word, streamer, sort = T)#, sort = TRUE)
top_10 <-word_cors %>% mutate("streamer" = case_when(
item2 == "trainwreckstv" ~ "trainwreckstv", # ahh,
item2 == "esfandtv" ~ "esfandtv",
item2 == "forsen" ~ "forsen",
item2 == "mizkif" ~ "mizkif",
item2 == "ludwig" ~ "ludwig",
item2 == "moonmoon" ~ "moonmoon",
item2 == "xqcow" ~ "xqcow",
item2 == "sykkuno" ~ "sykkuno",
item2 == "vadikus007" ~ "vadikus007",
item2 == "loltyler1" ~ "loltyler1",
TRUE ~ "WHO?"
)) # there are 83 unique streamers in the dataset, we should filter this some how. Either top 20, or maybe with chats > 100.
# Build a scraper that grabes the names of emotes for eache of the streamers?
#top_10 %>% group_by(streamer) %>% count() # strange numbers here, each streamer has same number?, because I filteer for top 10 above?
# from 2 mil rows
# to about 2k rows
test<-top_10 %>%
mutate(contains_emote = case_when(item1 %in% emote_data$emote_name ~ 1, TRUE ~ 0)) %>%
filter(contains_emote == 1) %>% # filtering for only emotes!
filter(streamer != "WHO?")%>%
group_by(streamer) %>% top_n(10,wt = correlation)
streamers = c("trainwreckstv","esfandtv","forsen","mizkif","ludwig","moonmoon","xqcow","sykkuno","vadikus007","loltyler1")
emote_data_1 <- emote_data %>% select(emote_name,emote_link) %>% na.omit()
# TEST 2
test_2 <- test %>% left_join(emote_data_1, by = c("item1" = "emote_name"))
#-------
test <- test %>% graph_from_data_frame()
test_viz <- toVisNetworkData(test)
test_viz$nodes <- test_viz$nodes %>% mutate("group" = case_when(label %in% streamers ~ "Streamer",TRUE ~ "Emote"))
#test_viz checking the dataframe
visNetwork(nodes = test_viz$nodes, edges = test_viz$edges, main = "Emote correlation to Streamer")%>%
visGroups(groupname = "Streamer", color = "green", shape = "square") %>%
visGroups(groupname = "Emote", color = "blue")%>%
visOptions(highlightNearest = list(enabled = T, hover = T))%>%
visLegend()#--------------------test_2 <- test_2 %>% graph_from_data_frame()
test_viz_2 <- toVisNetworkData(test_2)
test_viz_2$nodes %>% distinct()## id label
## 1 feelsokayman feelsokayman
## 2 pogyou pogyou
## 3 catdance catdance
## 4 feelsrainman feelsrainman
## 5 lulwut lulwut
## 6 wutfacew wutfacew
## 7 peepofat peepofat
## 8 nopers nopers
## 9 pepepains pepepains
## 10 dance dance
## 11 dicks dicks
## 12 pepem pepem
## 13 handsup handsup
## 14 zulul zulul
## 15 monkahmm monkahmm
## 16 pepocheer pepocheer
## 17 pepegapls pepegapls
## 18 kkomrade kkomrade
## 19 nodders nodders
## 20 poggies poggies
## 21 kapp kapp
## 22 nymncorn nymncorn
## 23 kissahomie kissahomie
## 24 waitwhat waitwhat
## 25 megalul megalul
## 26 pugpls pugpls
## 27 gachiw gachiw
## 28 omegalaughing omegalaughing
## 29 peeposhy peeposhy
## 30 yappp yappp
## 31 bboomer bboomer
## 32 pphop pphop
## 33 pepew pepew
## 34 monaks monaks
## 35 okayge okayge
## 36 pepela pepela
## 37 peped peped
## 38 tridance tridance
## 39 forsenpls forsenpls
## 40 feelsstrongman feelsstrongman
## 41 okaychamp okaychamp
## 42 pepepls pepepls
## 43 peepopog peepopog
## 44 trikool trikool
## 45 forsenscoots forsenscoots
## 46 headbang headbang
## 47 nyanpls nyanpls
## 48 okayeg okayeg
## 49 pepege pepege
## 50 zoomer zoomer
## 51 widehardo widehardo
## 52 pepes pepes
## 53 pepemeltdown pepemeltdown
## 54 ppl ppl
## 55 oooo oooo
## 56 sillychamp sillychamp
## 57 gachipls gachipls
## 58 teatime teatime
## 59 feelsbadman feelsbadman
## 60 gachihyper gachihyper
## 61 donowall donowall
## 62 kkonaw kkonaw
## 63 painschamp painschamp
## 64 monkaomega monkaomega
## 65 rip rip
## 66 peepoween peepoween
## 67 ppoverheat ppoverheat
## 68 pepega pepega
## 69 woah woah
## 70 pogo pogo
## 71 widepeeposad widepeeposad
## 72 hahaa hahaa
## 73 bruh bruh
## 74 oof oof
## 75 catjam catjam
## 76 gachibass gachibass
## 77 hypers hypers
## 78 xqcow xqcow
## 79 loltyler1 loltyler1
## 80 moonmoon moonmoon
## 81 trainwreckstv trainwreckstv
## 82 forsen forsen
## 83 esfandtv esfandtv
## 84 mizkif mizkif
## 85 ludwig ludwig
## 86 sykkuno sykkunotest_viz_2$edges %>% distinct()## from to correlation streamer contains_emote
## 1 feelsokayman xqcow 0.7114652 xqcow 1
## 2 pogyou loltyler1 0.7027819 loltyler1 1
## 3 catdance moonmoon 0.7027819 moonmoon 1
## 4 feelsrainman moonmoon 0.7027819 moonmoon 1
## 5 lulwut moonmoon 0.7027819 moonmoon 1
## 6 wutfacew trainwreckstv 0.7027819 trainwreckstv 1
## 7 peepofat trainwreckstv 0.7027819 trainwreckstv 1
## 8 nopers moonmoon 0.6983225 moonmoon 1
## 9 pepepains forsen 0.6937218 forsen 1
## 10 dance esfandtv 0.6541667 esfandtv 1
## 11 dicks esfandtv 0.6541667 esfandtv 1
## 12 feelsokayman forsen 0.6406926 forsen 1
## 13 pepem mizkif 0.6206329 mizkif 1
## 14 handsup xqcow 0.6206329 xqcow 1
## 15 zulul forsen 0.5888680 forsen 1
## 16 monkahmm mizkif 0.5743590 mizkif 1
## 17 pepocheer loltyler1 0.5702659 loltyler1 1
## 18 pepegapls trainwreckstv 0.5702659 trainwreckstv 1
## 19 kkomrade trainwreckstv 0.5702659 trainwreckstv 1
## 20 dance trainwreckstv 0.5702659 trainwreckstv 1
## 21 dicks trainwreckstv 0.5702659 trainwreckstv 1
## 22 nodders moonmoon 0.5629142 moonmoon 1
## 23 handsup forsen 0.5629142 forsen 1
## 24 poggies ludwig 0.5592057 ludwig 1
## 25 kapp mizkif 0.5181018 mizkif 1
## 26 nymncorn forsen 0.5159300 forsen 1
## 27 kissahomie mizkif 0.4935589 mizkif 1
## 28 dicks mizkif 0.4935589 mizkif 1
## 29 monkahmm esfandtv 0.4935589 esfandtv 1
## 30 monkahmm sykkuno 0.4935589 sykkuno 1
## 31 pepepains xqcow 0.4935589 xqcow 1
## 32 waitwhat trainwreckstv 0.4907684 trainwreckstv 1
## 33 megalul trainwreckstv 0.4907684 trainwreckstv 1
## 34 megalul trainwreckstv 0.4907684 trainwreckstv 1
## 35 pugpls moonmoon 0.4876543 moonmoon 1
## 36 gachiw moonmoon 0.4876543 moonmoon 1
## 37 omegalaughing moonmoon 0.4876543 moonmoon 1
## 38 peeposhy moonmoon 0.4876543 moonmoon 1
## 39 yappp moonmoon 0.4876543 moonmoon 1
## 40 bboomer moonmoon 0.4876543 moonmoon 1
## 41 pphop moonmoon 0.4876543 moonmoon 1
## 42 pepew moonmoon 0.4876543 moonmoon 1
## 43 monaks moonmoon 0.4876543 moonmoon 1
## 44 okayge moonmoon 0.4876543 moonmoon 1
## 45 pepela ludwig 0.4746835 ludwig 1
## 46 peped mizkif 0.4698137 mizkif 1
## 47 tridance mizkif 0.4698137 mizkif 1
## 48 forsenpls forsen 0.4610390 forsen 1
## 49 feelsstrongman mizkif 0.4481860 mizkif 1
## 50 okaychamp esfandtv 0.4444625 esfandtv 1
## 51 pepepls loltyler1 0.4361696 loltyler1 1
## 52 nymncorn loltyler1 0.4361696 loltyler1 1
## 53 peepopog trainwreckstv 0.4361696 trainwreckstv 1
## 54 trikool trainwreckstv 0.4361696 trainwreckstv 1
## 55 forsenscoots xqcow 0.4361696 xqcow 1
## 56 headbang xqcow 0.4361696 xqcow 1
## 57 nyanpls xqcow 0.4361696 xqcow 1
## 58 okayeg xqcow 0.4361696 xqcow 1
## 59 pepege xqcow 0.4361696 xqcow 1
## 60 zoomer xqcow 0.4361696 xqcow 1
## 61 widehardo mizkif 0.4320543 mizkif 1
## 62 pepes ludwig 0.4158886 ludwig 1
## 63 pepemeltdown ludwig 0.4158886 ludwig 1
## 64 ppl ludwig 0.4158886 ludwig 1
## 65 oooo xqcow 0.4158886 xqcow 1
## 66 sillychamp xqcow 0.4158886 xqcow 1
## 67 gachipls xqcow 0.4158886 xqcow 1
## 68 zulul xqcow 0.4158886 xqcow 1
## 69 teatime forsen 0.4144122 forsen 1
## 70 feelsbadman esfandtv 0.4123106 esfandtv 1
## 71 gachihyper mizkif 0.4074748 mizkif 1
## 72 donowall esfandtv 0.4057949 esfandtv 1
## 73 kkonaw esfandtv 0.4057949 esfandtv 1
## 74 painschamp esfandtv 0.4057949 esfandtv 1
## 75 kapp esfandtv 0.3962717 esfandtv 1
## 76 forsenscoots forsen 0.3956059 forsen 1
## 77 lulwut forsen 0.3956059 forsen 1
## 78 headbang forsen 0.3956059 forsen 1
## 79 nyanpls forsen 0.3956059 forsen 1
## 80 okayeg forsen 0.3956059 forsen 1
## 81 pepege forsen 0.3956059 forsen 1
## 82 pepem esfandtv 0.3905059 esfandtv 1
## 83 gachiw esfandtv 0.3905059 esfandtv 1
## 84 omegalaughing esfandtv 0.3905059 esfandtv 1
## 85 bboomer esfandtv 0.3905059 esfandtv 1
## 86 wutfacew esfandtv 0.3905059 esfandtv 1
## 87 peepofat esfandtv 0.3905059 esfandtv 1
## 88 monkaomega sykkuno 0.3905059 sykkuno 1
## 89 pogyou sykkuno 0.3905059 sykkuno 1
## 90 rip sykkuno 0.3815603 sykkuno 1
## 91 peepoween sykkuno 0.3742029 sykkuno 1
## 92 ppoverheat ludwig 0.3716412 ludwig 1
## 93 peped loltyler1 0.3638739 loltyler1 1
## 94 pepega sykkuno 0.3554093 sykkuno 1
## 95 woah ludwig 0.3493214 ludwig 1
## 96 pogo ludwig 0.3493214 ludwig 1
## 97 widepeeposad sykkuno 0.3476739 sykkuno 1
## 98 peepoween loltyler1 0.3381261 loltyler1 1
## 99 hahaa loltyler1 0.3381261 loltyler1 1
## 100 hahaa loltyler1 0.3381261 loltyler1 1
## 101 hahaa loltyler1 0.3381261 loltyler1 1
## 102 painschamp ludwig 0.3365734 ludwig 1
## 103 bruh ludwig 0.3331710 ludwig 1
## 104 oof sykkuno 0.3249166 sykkuno 1
## 105 kkomrade sykkuno 0.3083333 sykkuno 1
## 106 pepocheer sykkuno 0.3083333 sykkuno 1
## 107 poggies sykkuno 0.3083333 sykkuno 1
## 108 catjam loltyler1 0.2983696 loltyler1 1
## 109 gachibass loltyler1 0.2983696 loltyler1 1
## 110 hypers loltyler1 0.2983696 loltyler1 1
## emote_link
## 1 https://cdn.frankerfacez.com/emoticon/145947/1
## 2 https://cdn.frankerfacez.com/emoticon/297232/1
## 3 <NA>
## 4 <NA>
## 5 https://cdn.frankerfacez.com/emoticon/249979/1
## 6 https://cdn.frankerfacez.com/emoticon/168065/1
## 7 https://cdn.frankerfacez.com/emoticon/288800/1
## 8 <NA>
## 9 https://cdn.frankerfacez.com/emoticon/227992/1
## 10 <NA>
## 11 https://cdn.frankerfacez.com/emoticon/19972/1
## 12 https://cdn.frankerfacez.com/emoticon/145947/1
## 13 https://cdn.frankerfacez.com/emoticon/362432/1
## 14 https://cdn.frankerfacez.com/emoticon/229760/1
## 15 <NA>
## 16 https://cdn.frankerfacez.com/emoticon/240746/1
## 17 <NA>
## 18 <NA>
## 19 https://cdn.frankerfacez.com/emoticon/145916/1
## 20 <NA>
## 21 https://cdn.frankerfacez.com/emoticon/19972/1
## 22 <NA>
## 23 https://cdn.frankerfacez.com/emoticon/229760/1
## 24 https://cdn.frankerfacez.com/emoticon/257284/1
## 25 https://cdn.frankerfacez.com/emoticon/218860/1
## 26 <NA>
## 27 https://cdn.frankerfacez.com/emoticon/298316/1
## 28 https://cdn.frankerfacez.com/emoticon/19972/1
## 29 https://cdn.frankerfacez.com/emoticon/240746/1
## 30 https://cdn.frankerfacez.com/emoticon/240746/1
## 31 https://cdn.frankerfacez.com/emoticon/227992/1
## 32 https://cdn.frankerfacez.com/emoticon/47416/1
## 33 https://cdn.frankerfacez.com/emoticon/419215/1
## 34 https://cdn.frankerfacez.com/emoticon/119959/1
## 35 <NA>
## 36 <NA>
## 37 <NA>
## 38 https://cdn.frankerfacez.com/emoticon/448771/1
## 39 <NA>
## 40 <NA>
## 41 <NA>
## 42 https://cdn.frankerfacez.com/emoticon/232677/1
## 43 https://cdn.frankerfacez.com/emoticon/184836/1
## 44 https://cdn.frankerfacez.com/emoticon/410314/1
## 45 https://cdn.frankerfacez.com/emoticon/355871/1
## 46 https://cdn.frankerfacez.com/emoticon/319304/1
## 47 <NA>
## 48 <NA>
## 49 https://cdn.frankerfacez.com/emoticon/64210/1
## 50 https://cdn.frankerfacez.com/emoticon/280358/1
## 51 <NA>
## 52 <NA>
## 53 https://cdn.frankerfacez.com/emoticon/302472/1
## 54 https://cdn.frankerfacez.com/emoticon/375269/1
## 55 https://cdn.frankerfacez.com/emoticon/265650/1
## 56 <NA>
## 57 <NA>
## 58 https://cdn.frankerfacez.com/emoticon/438696/1
## 59 https://cdn.frankerfacez.com/emoticon/341610/1
## 60 https://cdn.frankerfacez.com/emoticon/316077/1
## 61 https://cdn.frankerfacez.com/emoticon/309114/1
## 62 <NA>
## 63 <NA>
## 64 https://cdn.frankerfacez.com/emoticon/229544/1
## 65 <NA>
## 66 https://cdn.frankerfacez.com/emoticon/368150/1
## 67 <NA>
## 68 <NA>
## 69 <NA>
## 70 https://cdn.frankerfacez.com/emoticon/33355/1
## 71 https://cdn.frankerfacez.com/emoticon/196406/1
## 72 <NA>
## 73 https://cdn.frankerfacez.com/emoticon/229486/1
## 74 https://cdn.frankerfacez.com/emoticon/263297/1
## 75 https://cdn.frankerfacez.com/emoticon/218860/1
## 76 https://cdn.frankerfacez.com/emoticon/265650/1
## 77 https://cdn.frankerfacez.com/emoticon/249979/1
## 78 <NA>
## 79 <NA>
## 80 https://cdn.frankerfacez.com/emoticon/438696/1
## 81 https://cdn.frankerfacez.com/emoticon/341610/1
## 82 https://cdn.frankerfacez.com/emoticon/362432/1
## 83 <NA>
## 84 <NA>
## 85 <NA>
## 86 https://cdn.frankerfacez.com/emoticon/168065/1
## 87 https://cdn.frankerfacez.com/emoticon/288800/1
## 88 https://cdn.frankerfacez.com/emoticon/167431/1
## 89 https://cdn.frankerfacez.com/emoticon/297232/1
## 90 https://cdn.frankerfacez.com/emoticon/164177/1
## 91 <NA>
## 92 <NA>
## 93 https://cdn.frankerfacez.com/emoticon/319304/1
## 94 https://cdn.frankerfacez.com/emoticon/243789/1
## 95 <NA>
## 96 https://cdn.frankerfacez.com/emoticon/401202/1
## 97 https://cdn.frankerfacez.com/emoticon/303899/1
## 98 <NA>
## 99 https://cdn.frankerfacez.com/emoticon/57611/1
## 100 https://cdn.frankerfacez.com/emoticon/285676/1
## 101 https://cdn.frankerfacez.com/emoticon/144811/1
## 102 https://cdn.frankerfacez.com/emoticon/263297/1
## 103 https://cdn.frankerfacez.com/emoticon/294111/1
## 104 https://cdn.frankerfacez.com/emoticon/261564/1
## 105 https://cdn.frankerfacez.com/emoticon/145916/1
## 106 <NA>
## 107 https://cdn.frankerfacez.com/emoticon/257284/1
## 108 <NA>
## 109 https://cdn.frankerfacez.com/emoticon/273779/1
## 110 https://cdn.frankerfacez.com/emoticon/236895/1test_viz_2$nodes <- test_viz_2$nodes %>% mutate("group" = case_when(label %in% streamers ~ "Streamer",TRUE ~ "Emote"),
"shape" = "image")
#test_viz_2$nodes <-
test_viz_2$nodes<-test_viz_2$nodes %>% left_join(test_viz_2$edges, by = c('id' = 'from')) %>% select(id,label,group,shape,emote_link) %>% rename(image = emote_link) %>% distinct(id, .keep_all = T)
test_viz_2$nodes %>% distinct()## id label group shape
## 1 feelsokayman feelsokayman Emote image
## 2 pogyou pogyou Emote image
## 3 catdance catdance Emote image
## 4 feelsrainman feelsrainman Emote image
## 5 lulwut lulwut Emote image
## 6 wutfacew wutfacew Emote image
## 7 peepofat peepofat Emote image
## 8 nopers nopers Emote image
## 9 pepepains pepepains Emote image
## 10 dance dance Emote image
## 11 dicks dicks Emote image
## 12 pepem pepem Emote image
## 13 handsup handsup Emote image
## 14 zulul zulul Emote image
## 15 monkahmm monkahmm Emote image
## 16 pepocheer pepocheer Emote image
## 17 pepegapls pepegapls Emote image
## 18 kkomrade kkomrade Emote image
## 19 nodders nodders Emote image
## 20 poggies poggies Emote image
## 21 kapp kapp Emote image
## 22 nymncorn nymncorn Emote image
## 23 kissahomie kissahomie Emote image
## 24 waitwhat waitwhat Emote image
## 25 megalul megalul Emote image
## 26 pugpls pugpls Emote image
## 27 gachiw gachiw Emote image
## 28 omegalaughing omegalaughing Emote image
## 29 peeposhy peeposhy Emote image
## 30 yappp yappp Emote image
## 31 bboomer bboomer Emote image
## 32 pphop pphop Emote image
## 33 pepew pepew Emote image
## 34 monaks monaks Emote image
## 35 okayge okayge Emote image
## 36 pepela pepela Emote image
## 37 peped peped Emote image
## 38 tridance tridance Emote image
## 39 forsenpls forsenpls Emote image
## 40 feelsstrongman feelsstrongman Emote image
## 41 okaychamp okaychamp Emote image
## 42 pepepls pepepls Emote image
## 43 peepopog peepopog Emote image
## 44 trikool trikool Emote image
## 45 forsenscoots forsenscoots Emote image
## 46 headbang headbang Emote image
## 47 nyanpls nyanpls Emote image
## 48 okayeg okayeg Emote image
## 49 pepege pepege Emote image
## 50 zoomer zoomer Emote image
## 51 widehardo widehardo Emote image
## 52 pepes pepes Emote image
## 53 pepemeltdown pepemeltdown Emote image
## 54 ppl ppl Emote image
## 55 oooo oooo Emote image
## 56 sillychamp sillychamp Emote image
## 57 gachipls gachipls Emote image
## 58 teatime teatime Emote image
## 59 feelsbadman feelsbadman Emote image
## 60 gachihyper gachihyper Emote image
## 61 donowall donowall Emote image
## 62 kkonaw kkonaw Emote image
## 63 painschamp painschamp Emote image
## 64 monkaomega monkaomega Emote image
## 65 rip rip Emote image
## 66 peepoween peepoween Emote image
## 67 ppoverheat ppoverheat Emote image
## 68 pepega pepega Emote image
## 69 woah woah Emote image
## 70 pogo pogo Emote image
## 71 widepeeposad widepeeposad Emote image
## 72 hahaa hahaa Emote image
## 73 bruh bruh Emote image
## 74 oof oof Emote image
## 75 catjam catjam Emote image
## 76 gachibass gachibass Emote image
## 77 hypers hypers Emote image
## 78 xqcow xqcow Streamer image
## 79 loltyler1 loltyler1 Streamer image
## 80 moonmoon moonmoon Streamer image
## 81 trainwreckstv trainwreckstv Streamer image
## 82 forsen forsen Streamer image
## 83 esfandtv esfandtv Streamer image
## 84 mizkif mizkif Streamer image
## 85 ludwig ludwig Streamer image
## 86 sykkuno sykkuno Streamer image
## image
## 1 https://cdn.frankerfacez.com/emoticon/145947/1
## 2 https://cdn.frankerfacez.com/emoticon/297232/1
## 3 <NA>
## 4 <NA>
## 5 https://cdn.frankerfacez.com/emoticon/249979/1
## 6 https://cdn.frankerfacez.com/emoticon/168065/1
## 7 https://cdn.frankerfacez.com/emoticon/288800/1
## 8 <NA>
## 9 https://cdn.frankerfacez.com/emoticon/227992/1
## 10 <NA>
## 11 https://cdn.frankerfacez.com/emoticon/19972/1
## 12 https://cdn.frankerfacez.com/emoticon/362432/1
## 13 https://cdn.frankerfacez.com/emoticon/229760/1
## 14 <NA>
## 15 https://cdn.frankerfacez.com/emoticon/240746/1
## 16 <NA>
## 17 <NA>
## 18 https://cdn.frankerfacez.com/emoticon/145916/1
## 19 <NA>
## 20 https://cdn.frankerfacez.com/emoticon/257284/1
## 21 https://cdn.frankerfacez.com/emoticon/218860/1
## 22 <NA>
## 23 https://cdn.frankerfacez.com/emoticon/298316/1
## 24 https://cdn.frankerfacez.com/emoticon/47416/1
## 25 https://cdn.frankerfacez.com/emoticon/419215/1
## 26 <NA>
## 27 <NA>
## 28 <NA>
## 29 https://cdn.frankerfacez.com/emoticon/448771/1
## 30 <NA>
## 31 <NA>
## 32 <NA>
## 33 https://cdn.frankerfacez.com/emoticon/232677/1
## 34 https://cdn.frankerfacez.com/emoticon/184836/1
## 35 https://cdn.frankerfacez.com/emoticon/410314/1
## 36 https://cdn.frankerfacez.com/emoticon/355871/1
## 37 https://cdn.frankerfacez.com/emoticon/319304/1
## 38 <NA>
## 39 <NA>
## 40 https://cdn.frankerfacez.com/emoticon/64210/1
## 41 https://cdn.frankerfacez.com/emoticon/280358/1
## 42 <NA>
## 43 https://cdn.frankerfacez.com/emoticon/302472/1
## 44 https://cdn.frankerfacez.com/emoticon/375269/1
## 45 https://cdn.frankerfacez.com/emoticon/265650/1
## 46 <NA>
## 47 <NA>
## 48 https://cdn.frankerfacez.com/emoticon/438696/1
## 49 https://cdn.frankerfacez.com/emoticon/341610/1
## 50 https://cdn.frankerfacez.com/emoticon/316077/1
## 51 https://cdn.frankerfacez.com/emoticon/309114/1
## 52 <NA>
## 53 <NA>
## 54 https://cdn.frankerfacez.com/emoticon/229544/1
## 55 <NA>
## 56 https://cdn.frankerfacez.com/emoticon/368150/1
## 57 <NA>
## 58 <NA>
## 59 https://cdn.frankerfacez.com/emoticon/33355/1
## 60 https://cdn.frankerfacez.com/emoticon/196406/1
## 61 <NA>
## 62 https://cdn.frankerfacez.com/emoticon/229486/1
## 63 https://cdn.frankerfacez.com/emoticon/263297/1
## 64 https://cdn.frankerfacez.com/emoticon/167431/1
## 65 https://cdn.frankerfacez.com/emoticon/164177/1
## 66 <NA>
## 67 <NA>
## 68 https://cdn.frankerfacez.com/emoticon/243789/1
## 69 <NA>
## 70 https://cdn.frankerfacez.com/emoticon/401202/1
## 71 https://cdn.frankerfacez.com/emoticon/303899/1
## 72 https://cdn.frankerfacez.com/emoticon/57611/1
## 73 https://cdn.frankerfacez.com/emoticon/294111/1
## 74 https://cdn.frankerfacez.com/emoticon/261564/1
## 75 <NA>
## 76 https://cdn.frankerfacez.com/emoticon/273779/1
## 77 https://cdn.frankerfacez.com/emoticon/236895/1
## 78 <NA>
## 79 <NA>
## 80 <NA>
## 81 <NA>
## 82 <NA>
## 83 <NA>
## 84 <NA>
## 85 <NA>
## 86 <NA>#test_viz_2 checking the dataframe
visNetwork(nodes = test_viz_2$nodes, edges = test_viz_2$edges, main = "Emote correlation to Streamer")%>%
visGroups(groupname = "Streamer", color = "green", shape = "square") %>%
visGroups(groupname = "Emote", color = "blue")%>%
visOptions(highlightNearest = list(enabled = T, hover = T))%>%
visLegend()
# delete me at some point
test<-top_10 %>%
mutate(contains_emote = case_when(item1 %in% emote_data$emote_name ~ 1, TRUE ~ 0)) %>%
filter(contains_emote == 1) %>% # filtering for only emotes!
filter(streamer != "WHO?")%>%
inner_join(emote_data, by = c("item1" = "emote_name")) %>%
filter(emote_link != is.na(emote_link)) %>%
group_by(streamer) %>% top_n(10,wt = correlation) %>%
graph_from_data_frame()# %>%
# ggraph(layout = "fr") +
# geom_edge_link(aes(edge_alpha = correlation), show.legend = FALSE) +
# geom_node_point(aes(color = streamer), size = 5) +
# geom_node_text(aes(label = name), repel = TRUE) +
# theme_void()
streamers = c("trainwreckstv","esfandtv","forsen","mizkif","ludwig","moonmoon","xqcow","sykkuno","vadikus007","loltyler1")
test_viz <- toVisNetworkData(test)
test_viz$edges
test_viz$nodes
test_viz$nodes <- test_viz$nodes %>%
mutate("group" = case_when(label %in% streamers ~ "Streamer",TRUE ~ "Emote"),
"shape" = "image") %>%
inner_join(test_viz$edges, by = c("id" = "from")) %>%
select(c(id,label,group,shape,emote_link))%>%
mutate("image" = magick::image_read(emote_link))
#test_viz checking the dataframe
visNetwork(nodes = test_viz$nodes, edges = test_viz$edges, main = "Emote correlation to Streamer")%>%
visGroups(groupname = "Streamer", color = "green", shape = "square") %>%
visGroups(groupname = "Emote", color = "blue")%>%
visOptions(highlightNearest = list(enabled = T, hover = T))%>%
visLegend()